Given a network \(G=\big ( V,E \big )\)  with a set of vertices \(V\)  and their corresponding labels \(Y\) , node classification is learning a mapping M:\(V \rightarrow Y\) . The node embedding extracted from other layers of the multilayer network using Multi-Net or other ebedding techniques [1]} [2]} [3]} can be given as input to a classifier like SVMs, Neural Networks, such that the classifier learns node features and their corresponding labels. With proper parameter tuning in the classifier and cross validations, we can optimize the parameters used for node embeddings models.
